Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly?
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Abstract (arXiv)
As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media we analyze eleven million posts and investigate propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus sub-population behaviour (by demographics) when spreading misinformation, and distinguish between two types of propagation, i.e., direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulationscited by 3
- 2021Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languagescited by 2
- 2022MEWS: Real-time Social Media Manipulation Detection and Analysiscited by 0
- 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations24 Aug 2023
- 2022MEWS: Real-time Social Media Manipulation Detection and Analysis11 May 2022
- 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media21 Apr 2022
- 2021Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages23 Apr 2021